{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "ri = pd.read_csv('police.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(91741, 15)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ri.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "combine=ri.stop_date.str.cat(ri.stop_time,sep=' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "ri['dt']=pd.to_datetime(combine)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "ri"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    90926\n",
       "True       815\n",
       "Name: drugs_related_stop, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ri.drugs_related_stop.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b13ab18e48>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ri.groupby(ri.dt.dt.hour).drugs_related_stop.mean().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
